Peter Claes has obtained a PhD in engineering in June 2007, within the field of medical image computing under the supervision of Prof. Paul Suetens and Prof. Dirk Vandermeulen at the KU Leuven, Belgium. During his PhD he developed a computer-based craniofacial reconstruction approach for victim identification purposes. After his PhD, he established his own research trajectory and vision, with fundamental interest in pattern recognition and predictive modeling within computational imaging and biology.

He was a Post-Doc at the, Melbourne Dental School, University of Melbourne, Australia, until January 2011. During that time, he built an international and versatile network of collaborations that is still very useful and active today. Currently, he's appointed as honorary fellow at the Murdoch Children’s Research Institute, Australia and as research expert at the KU Leuven, ESAT/PSI/MIC, Belgium.

Within the position of research expert, the task is to independently establish a new line of research. To this end, he has focused on computational biology more in particular on 3D morphometric analyses from image data, with a series of granted project proposals both as partner as well as chief or sole investigator. The basis of his line of research lies in computer vision and medical image analysis with gained and proven extensions in biostatistics, genetics, human biology and disease as well as cognitive psychology. The motivation behind his line of research lies in creative thinking and the ability to image and visualize previously unexposed biological phenomena that lead to predictions based on biological relationships.

He prefers to be creative and to develop novel concepts (examples include dysmorphometrics, which provides a theoretical foundation for the measurement of morphological abnormalities, and bootstrapped response-based imputation modeling (BRIM), which is a novel relationship modeling technique that innovatively deals with imprecision in predictor variables) that lead to novel applications (such as modeling facial shape from DNA and the synthetic unaffected twin for patient-specific surgery planning).